ENH Adds plot_det_curve and associated display

doc/modules/classes.rst

@@ -947,7 +947,7 @@ details.
    metrics.cohen_kappa_score
    metrics.confusion_matrix
    metrics.dcg_score
-   metrics.detection_error_tradeoff_curve
+   metrics.det_curve
    metrics.f1_score
    metrics.fbeta_score
    metrics.hamming_loss
@@ -1100,6 +1100,7 @@ See the :ref:`visualizations` section of the user guide for further details.
    :template: function.rst
 
    metrics.plot_confusion_matrix
+   metrics.plot_det_curve
    metrics.plot_precision_recall_curve
    metrics.plot_roc_curve
 
@@ -1108,6 +1109,7 @@ See the :ref:`visualizations` section of the user guide for further details.
    :template: class.rst
 
    metrics.ConfusionMatrixDisplay
+   metrics.DetCurveDisplay
    metrics.PrecisionRecallDisplay
    metrics.RocCurveDisplay
 

doc/modules/model_evaluation.rst

@@ -306,7 +306,7 @@ Some of these are restricted to the binary classification case:
 
    precision_recall_curve
    roc_curve
-   detection_error_tradeoff_curve
+   det_curve
 
 
 Others also work in the multiclass case:
@@ -1443,7 +1443,7 @@ to the given limit.
 Detection error tradeoff (DET)
 ------------------------------
 
-The function :func:`detection_error_tradeoff_curve` computes the
+The function :func:`det_curve` computes the
 detection error tradeoff curve (DET) curve [WikipediaDET2017]_.
 Quoting Wikipedia:
 

doc/visualizations.rst

@@ -78,6 +78,7 @@ Functions
 
    inspection.plot_partial_dependence
    metrics.plot_confusion_matrix
+   metrics.plot_det_curve
    metrics.plot_precision_recall_curve
    metrics.plot_roc_curve
 
@@ -91,5 +92,6 @@ Display Objects
 
    inspection.PartialDependenceDisplay
    metrics.ConfusionMatrixDisplay
+   metrics.DetCurveDisplay
    metrics.PrecisionRecallDisplay
    metrics.RocCurveDisplay

doc/whats_new/v0.24.rst

@@ -280,11 +280,15 @@ Changelog
 :mod:`sklearn.metrics`
 ......................
 
-- |Feature| Added :func:`metrics.detection_error_tradeoff_curve` to compute
-  Detection Error Tradeoff curve classification metric.
+- |Feature| Added :func:`metrics.det_curve` to compute Detection Error Tradeoff
+  curve classification metric.
   :pr:`10591` by :user:`Jeremy Karnowski <jkarnows>` and
   :user:`Daniel Mohns <dmohns>`.
 
+- |Feature| Added :func:`metrics.plot_det_curve` and :class:`DetCurveDisplay`
+  to ease the plot of DET curves.
+  :pr:`18176` by :user:`Guillaume Lemaitre <glemaitre>`.
+
 - |Feature| Added :func:`metrics.mean_absolute_percentage_error` metric and
   the associated scorer for regression problems. :issue:`10708` fixed with the
   PR :pr:`15007` by :user:`Ashutosh Hathidara <ashutosh1919>`. The scorer and

examples/model_selection/plot_det.py

@@ -8,9 +8,9 @@
 for the same classification task.
 
 DET curves are commonly plotted in normal deviate scale.
-To achieve this we transform the error rates as returned by the
-:func:`~sklearn.metrics.detection_error_tradeoff_curve` function and the axis
-scale using :func:`scipy.stats.norm`.
+To achieve this `plot_det_curve` transforms the error rates as returned by the
+:func:`~sklearn.metrics.det_curve` and the axis scale using
+:func:`scipy.stats.norm`.
 
 The point of this example is to demonstrate two properties of DET curves,
 namely:
@@ -39,8 +39,8 @@
     - See :func:`sklearn.metrics.roc_curve` for further information about ROC
       curves.
 
-    - See :func:`sklearn.metrics.detection_error_tradeoff_curve` for further
-      information about DET curves.
+    - See :func:`sklearn.metrics.det_curve` for further information about
+      DET curves.
 
     - This example is loosely based on
       :ref:`sphx_glr_auto_examples_classification_plot_classifier_comparison.py`
@@ -51,15 +51,13 @@
 
 from sklearn.datasets import make_classification
 from sklearn.ensemble import RandomForestClassifier
-from sklearn.metrics import detection_error_tradeoff_curve
+from sklearn.metrics import plot_det_curve
 from sklearn.metrics import plot_roc_curve
 from sklearn.model_selection import train_test_split
 from sklearn.pipeline import make_pipeline
 from sklearn.preprocessing import StandardScaler
 from sklearn.svm import LinearSVC
 
-from scipy.stats import norm
-
 N_SAMPLES = 1000
 
 classifiers = {
@@ -79,43 +77,17 @@
 # prepare plots
 fig, [ax_roc, ax_det] = plt.subplots(1, 2, figsize=(11, 5))
 
-# first prepare the ROC curve
-ax_roc.set_title('Receiver Operating Characteristic (ROC) curves')
-ax_roc.grid(linestyle='--')
-
-# second prepare the DET curve
-ax_det.set_title('Detection Error Tradeoff (DET) curves')
-ax_det.set_xlabel('False Positive Rate')
-ax_det.set_ylabel('False Negative Rate')
-ax_det.set_xlim(-3, 3)
-ax_det.set_ylim(-3, 3)
-ax_det.grid(linestyle='--')
-
-# customized ticks for DET curve plot to represent normal deviate scale
-ticks = [0.001, 0.01, 0.05, 0.20, 0.5, 0.80, 0.95, 0.99, 0.999]
-tick_locs = norm.ppf(ticks)
-tick_lbls = [
-    '{:.0%}'.format(s) if (100*s).is_integer() else '{:.1%}'.format(s)
-    for s in ticks
-]
-plt.sca(ax_det)
-plt.xticks(tick_locs, tick_lbls)
-plt.yticks(tick_locs, tick_lbls)
-
-# iterate over classifiers
 for name, clf in classifiers.items():
     clf.fit(X_train, y_train)
 
-    if hasattr(clf, "decision_function"):
-        y_score = clf.decision_function(X_test)
-    else:
-        y_score = clf.predict_proba(X_test)[:, 1]
-
     plot_roc_curve(clf, X_test, y_test, ax=ax_roc, name=name)
-    det_fpr, det_fnr, _ = detection_error_tradeoff_curve(y_test, y_score)
+    plot_det_curve(clf, X_test, y_test, ax=ax_det, name=name)
+
+ax_roc.set_title('Receiver Operating Characteristic (ROC) curves')
+ax_det.set_title('Detection Error Tradeoff (DET) curves')
 
-    # transform errors into normal deviate scale
-    ax_det.plot(norm.ppf(det_fpr), norm.ppf(det_fnr), label=name)
+ax_roc.grid(linestyle='--')
+ax_det.grid(linestyle='--')
 
 plt.legend()
 plt.show()

sklearn/metrics/__init__.py

@@ -7,7 +7,7 @@
 from ._ranking import auc
 from ._ranking import average_precision_score
 from ._ranking import coverage_error
-from ._ranking import detection_error_tradeoff_curve
+from ._ranking import det_curve
 from ._ranking import dcg_score
 from ._ranking import label_ranking_average_precision_score
 from ._ranking import label_ranking_loss
@@ -77,6 +77,8 @@
 from ._scorer import SCORERS
 from ._scorer import get_scorer
 
+from ._plot.det_curve import plot_det_curve
+from ._plot.det_curve import DetCurveDisplay
 from ._plot.roc_curve import plot_roc_curve
 from ._plot.roc_curve import RocCurveDisplay
 from ._plot.precision_recall_curve import plot_precision_recall_curve
@@ -105,7 +107,8 @@
     'coverage_error',
     'dcg_score',
     'davies_bouldin_score',
-    'detection_error_tradeoff_curve',
+    'DetCurveDisplay',
+    'det_curve',
     'euclidean_distances',
     'explained_variance_score',
     'f1_score',
@@ -142,6 +145,7 @@
     'pairwise_distances_chunked',
     'pairwise_kernels',
     'plot_confusion_matrix',
+    'plot_det_curve',
     'plot_precision_recall_curve',
     'plot_roc_curve',
     'PrecisionRecallDisplay',